Drug screening using a responsome profile generated on subject-derived samples

Abstract

The present invention relates to a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject using patient-derived samples in response to the exposure of drugs or combinations of drugs, comprising determining a responsome profile. The present invention further relates to high-throughput screening in patient-derived samples with minimal material requirement, and also comprises machine-leaming-based analysis.

Description

[0001] Drug screening using a responsome profile generated on subject-derived samples

[0002] The present invention relates to a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject using subject-derived samples in response to the exposure of drugs or combinations of drugs, comprising determining a responsome profile. The present invention further relates to high- throughput screening in patient-derived samples with minimal material requirement, and also comprises machine-leaming-based analysis.

[0003] Background of the invention

[0004] Anti-proliferative, such as cancer therapies have so far provided limited efficacy and only in a small subset of patients, despite major efforts to characterize patients biologically and genomically to find response biomarkers.

[0005] Predicting the patient specific response of a certain treatment is a crucial step for identifying the optimal therapy, especially in personalized medicine. To ensure the most accurate prediction of treatment response during the whole course of discovery of a drug or drug combination, preclinical testing and clinical testing as well as the use against proliferative diseases, such prediction should ideally be done in a system that most closely mimics the in vivo environment of the disease in a specific patient.

[0006] An approach that holds the promise to improve this situation is to complement large-genomic profiling in basal conditions with measurements after perturbing cancer cells with drugs. Nevertheless, this approach is limited by the numbers of assays required for a meaningful analysis, which gets more pronounced when considering drug combinations due to the sheer number of potential combinations, which increases exponentially with the number of tested drugs.

[0007] Another crucial factor is time, as the rate of a successful treatment of a proliferative disease decreases drastically over time. Also, the development of drug resistance may pose problems for an effective treatment. Due to limited screening capacities, computational approaches to model drug-drug interactions have been developed. While models on drug efficacies improved over the past years by an increase in available data resources, predictions on drug responses remain challenging and limited to well-characterized systems such as cell lines, thereby limiting their translatability into clinics. Among the different data types, gene expression states of cells were shown to be highly predictive of drug response.

[0008] 3D microtissues, particularly 3D microtumors, which are micro-physiological systems that can be used to model both normal and pathological human organs in vitro, are of growing interest, as they capture key structural and physiological characteristics of the target tissue. This technique ideally incorporates human cells and / or tissues, such as tumor biopsies, together with other cell types such as immune cells to form a cell aggregate that grows in a 3D extracellular matrix and that depends on nutrient delivery through living, perfused micro-vessels for survival.

[0009] Since 3D microtissues can be cultured from patient-derived materials, they have the advantage of generating a personalized treatment prediction based on individually curated data.

[0010] Guilliams M et al. (in: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell. 2022 Jan 20; 185(2)379-396. e38. doi: 10.1016 / j.cell.2021.12.018. Epub 2022 Jan 11. PMID: 35021063; PMCID: PMC8809252) disclose a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, they provide strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. They describe the respective spatially resolved cellular niches of macrophages and the microenvironmental circuits driving their unique transcriptomic identities.

[0011] WO 2021 / 110799A2 discloses the method and device for the analysis of such 3D microtumors for a systematic screening of a potential drug or drug combination. However, the method and device disclosed in this application for determining the efficacies of the tested drugs rely solely on parameters regarding the survival or growth of the 3D microtissues, such as the size, internal reporter gene expression, released biomarker levels (like LDH-release), which may not fully capture the complexity of treatment response. Since the outcome of a treatment is certainly the result of a much more complicated interplay of many biological factors, a more holistic approach that considers the entirety of the conditions of the test subject is required for a faster and more effective treatment outcome analysis.

[0012] Specifically, in the field of cancer treatment, the need for a method that considers the holistic situation of a specific patient is even more pressing due to the multifactorial nature of the disease. Though our understanding of the molecular basis of cancer has contributed significantly to the development of many targeted therapies, identifying which therapy might be suitable for a specific patient is not trivial, as individual therapy is often only effective for a small subset of patients. So, in practice, treatment selection is often done on a trial-and-error basis guided by the experience of individual physicians.

[0013] One example is the treatment of lung cancer patients with PD-1 / PD-L1 axis blocker, which is effective but only in a fraction of patients with no clear understanding of the specific mechanisms how the lung cancer cells in each individual patients may evade the treatment. This problem is partly attributed to the lack of high throughput, patient-specific therapeutic screening techniques, which restricts the number of drugs that can be screened as well as the type of physiological indicators that can be measured from patient-derived samples.

[0014] Mathur et. al. (in "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets." Nature communications 13.1 (2022): 4450) proposes a solution with a microfluid-based system, wherein the changes in the transcriptome in cancer cells under hundreds of different test conditions can be acquired. However, in this approach, a combination of maximum two drugs can be tested, and the system is designed exclusively for monitoring the transcriptome. In addition, in this system, indicators of tumor cell survival i.e., the efficacy of the drug or drug combination, cannot be measured directly, but rather inferred indirectly from the transcriptomic profile.

[0015] Thus, it is an object of the present invention to provide an improved method for identifying drugs and combinations of drugs for the fast and effective treatment of proliferative diseases based on cells and / or tumor tissues that are derived from subject biopsies or resections, in order to be able to assess a more complete picture of the effect(s) of one or a combination of drugs on the said cell and / or tumor tissue sample and thus to determine and provide the optimal and fastest therapy for the said patient or a group of patients. Other objects and advantages will readily become apparent for the person of skill from studying the following more detailed description and examples.

[0016] In a first aspect of the present invention, the problem of the present invention is solved by providing a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the steps of a) Providing an array of i) primary proliferative disease tissue samples and / or ii) proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, from a subject having or being diagnosed for a proliferative disease in a multi-well format in a suitable culture medium, wherein the multi-well format comprises a plurality of wells, such as between 6 wells and a number of wells corresponding to the drugs and / or combinations of drugs and doses thereof to be tested plus controls, b) Contacting the 3D microtissues, in particular 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined amount of time or predefined different amounts of time, c) Determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, based on i) a first step of a non-disruptive kineticbased functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of absolute number of cells, proliferation rate, growth kinetics, density or compactness of cells, degree of dissolution of cells, morphological changes of the cells, size of cells, cellular composition of the cells, tissues, tumors or organoids, and metabolic rate, and ii) a second step of a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, genetic mutation, and selected biomarker level analysis, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) Identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined in step c). Preferred is the method according to the present invention, wherein determining the responsome profile further comprises at least one analysis selected from the group consisting of i) for non- disruptive kinetic-based functional analysis, cell count, size determination of said 3D microtumor or organoid, determination of the LDH release of said 3D microtumor or organoid, and determination of pre-selected biomarkers in said 3D microtumor or organoid, intracellular calcium level, physiological parameters that directly indicate the absolute number of cells, the proliferation rate, and the metabolic rate, wherein preferably said size determination of said 3D microtumor or organoid comprises at least one parameter selected from diameter, perimeter, volume, and area of optical cross section, and wherein preferably said size determination of said 3D microtumor or organoid comprises the use of an imaging device, and optionally further comprising the analysis of growth-kinetics, and ii) for the molecular level parameter analysis, gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as, for example, PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch-repair deficiency (dMMR).

[0017] Preferred is the method according to the present invention, wherein determining the responsome profile further comprises including patient treatment outcome data (see also below).

[0018] Further preferred is the method according to the present invention, wherein before the contacting in step b) the primary proliferative disease tissue samples or the proliferative disease- derived 3D microtissues, in particular 3D microtumors or organoids, comprise cells reflecting the composition of the major cell types of the native tumor tissue, such as, for example, connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells, and CD8+ T lymphocytes.

[0019] Further preferred is the method according to the present invention, further comprising at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs, preferably further comprising the step of identifying genes and / or proteins of the pathway as drug targets. The present method does therefore not only identify personalized effective treatment options, the responsome here is also a tool to gain more knowledge about why in certain patient populations an anti-proliferative therapy that is supposed to work is not effective. The responsome will help to gain more knowledge on the patient and drug-specific responses to make therapies more effective and to address larger patient populations with the same effective therapy.

[0020] In a second aspect of the present invention, the above object is solved by providing a method for identifying adverse effects associated with a treatment with a patient-specific drug or combination of drugs in a subject, comprising performing the method according to the present invention, further comprising the step of testing and analyzing said subject-specific drug or combination of drugs for adverse effects in said patient.

[0021] In a third aspect of the present invention, the above object is solved by providing a method for stratifying a patient with respect to a treatment with a patient-specific drug or combination of drugs, comprising performing the method according to the present invention, comprising identifying at least one subject-specific drug or combination of drugs based on the responsome profile, and optionally further based on the molecular pathway, as determined, and stratifying of said subject based on said subject-specific drug or combination of drugs as identified.

[0022] In a fourth aspect of the present invention, the above object is solved by providing a method for monitoring the treatment with a subject-specific drug or combination of drugs, comprising performing the method according to the present invention, treating said patient with said at least one patient-specific drug or combination of drugs as identified, and repeating the method according to the present invention on a sample of said subject following the treatment, and optionally adjusting the treatment based on the result(s) of said repeating.

[0023] In a fifth aspect of the present invention, the above object is solved by providing a computer program for performing the method according to the present invention on a suitable computer. Preferred is the computer program according to the present invention, wherein the machine learning algorithm comprises a deep learning algorithm, for example a deep neural networks algorithm.

[0024] In a sixth aspect of the present invention, the above object is solved by providing a drug or combination of drugs for use in the prevention or treatment of a proliferative disease, wherein said treatment comprises performing the method according to the present invention, preferably further comprising the step of providing a subject-specific and / or personalized prevention or treatment. In a seventh aspect of the present invention, the above object is solved by providing a testing system comprising means for performing the method according to the present invention, comprising a) a unit for culturing an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample derived from the subject as provided, b) a drug testing unit for contacting said array of said 3D microtissues with at least one drugs or combination of drugs to be tested, c) a unit for determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, comprising i) a first analysis unit for a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, ii) a second analysis unit for determining a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined.

[0025] Preferred is the testing system according to the present invention, further comprising at least one database for collecting and / or storing data regarding the responsome profile as identified, and optionally additional data with respect to the pre-selection and / or grouping of the combination of drugs, physiological parameters, data with respect to the effect(s) of said drugs or combination of drugs thereof, data with respect to the cellular and / or tissue pathway-specific responsome analysis, data with respect to adverse effects, data with respect to additional clinical parameters, such as patient specific data and / or treatment history, data with respect to the stratification and / or monitoring, and data for automatization of the system, for example for controlling at least one robot.

[0026] In an eighth aspect of the present invention, the above object is solved by providing the use of the testing system according to the present invention or the computer program according to the present invention for identifying at least one subject-specific drug or combination of drugs and / or for at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs according to the method according to the present invention. As mentioned above, in a first aspect thereof, the present invention provides a method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the steps of a) Providing an array of i) primary proliferative disease tissue samples and / or ii) proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, from a subject having or being diagnosed for a proliferative disease in a multi-well format in a suitable culture medium, wherein the multi-well format comprises between 5 wells and a number of wells corresponding to the drugs and / or combinations of drugs and doses thereof to be tested plus controls, b) Contacting the 3D microtissues, in particular 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined amount of time or predefined different amounts of time, c) Determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses (e.g. physiologically relevant doses derived from the human Cmax value of a drug (see also below)), and times thereof as tested, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of absolute number of cells, proliferation rate, growth kinetics, density or compactness of cells, degree of dissolution of cells, morphological changes of the cells, size of cells, cellular composition of the cells, tissues, tumors or organoids, and metabolic rate, and ii) a second step of a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, genetic mutation, and selected biomarker level analysis, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) Identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined in step c).

[0027] The present invention relates to a novel approach for identifying specific and effective, in particular personalized, drug or combination of drugs against proliferative diseases in a subject. In order to maximize the information content derived from subject-specific samples, in particular tumor-derived samples, a method has been developed to obtain quick in-depth information of the effects and efficacies of a drug and / or drug combinations on the samples as tested, and in a preferred embodiment the underlying impact on specific cellular pathways using one and the same patient-specimen. In doing so, the present invention relates to methods and devices to analyze sets of physiological parameters with subject-derived samples in response to the exposure of drugs or combinations of drugs, in the form of the responsome profile, to the determination of an optimal personalized treatment, the identification of cellular pathways involved in the drug action, and ultimately the identification of new and promising drug targets. A particular advantage of the method according to the invention is therefore the possibility to identify patient- and drug-specific changes in intercellular signaling pathways as a function of drug efficacy.

[0028] The present invention provides fast, high-throughput drug effect screening on subject-derived microtissue or microtumor samples with a minimal material requirement, and therefore ultimately enables a fast analysis based on a more complete and complex drug profiling and including additional information, and preferably including machine-leaming-based analysis.

[0029] As an example of a preferred embodiment, a lung cancer tissue resection was obtained from a patient, dissociated to generate a single cell solution and distributed with a defined cell number in a non-adhesive, u-bottom multi-well plate to allow for gravity-enforced tumor tissue formation. After microtumor maturation of 5 days, microtumors were treated with either immune-checkpoint inhibitor monotherapies or in combination with chemotherapies, and the response monitored over 14 days. Drug response was analyzed via image-based (non-disruptive kinetic-based functional analysis) measuring of growth kinetics, subsequently RNA isolation of the very same specimen(s) was used to analyze transcriptomic changes (molecular level parameter-based analysis).

[0030] While there are already screening platforms available in plates for bulk and single-cell transcriptomics, they require large numbers of cells per tested condition, and thus have not been used for screening drug combinations. Furthermore, many of these assays relied solely on cell lines that do no longer resemble the actual tumor- and patient-specific situation, and therefore only provide a limited picture for the actual patient and situation.

[0031] Therefore, the integration of, for example, transcriptomic readouts into a miniaturized combinatorial drug screening platform having the potential to screen, for example, tumor biopsies will enable more relevant predictions and increase the understanding of the mode of action of synergistic and antagonistic drug-drug interactions. In addition, the possibility of creating an overview over a substantial number of possible therapies when using many available or newly identified drugs or combinations thereof in the present method according to the invention will result in a “catalogue” of new and more effective therapeutic approaches.

[0032] In the context of the present invention, a “responsome profile” is determined for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested. The “profile” relates to the determination of changes in selected responsome markers and / or parameters in response to the drugs or combinations of drugs tested.

[0033] In the context of the present invention, the term “responsome” shall mean to include a set or collection of selected biological, morphological and / or physiological markers and / or parameters as described herein that are tested for changes in response to the contacting with the drug or combination of drugs. This set includes at least a first step of a (at least one) non- disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of an array as provided, and ii) a (at least one) second step of a molecular level parameter-based analysis of the same 3D microtissues, in particular of the 3D microtumors or organoids of an array as provided. The analysis of responsome may, for example, include at least one analysis selected from the group consisting of absolute number of cells, proliferation rate, growth kinetics, density or compactness of cells, degree of dissolution of cells, morphological changes of the cells, size of cells, cellular composition of the cells, tissues, tumors or organoids, and metabolic rate as non-disruptive (i.e. substantially imparting cellular integrity), the metabolome, the proteome, genetic mutations, and selected biomarker level and / or expression analysis, the transcriptome, and / or the genome (i.e. molecular level parameter based) in the 3D microtissues, in particular of the 3D microtumors or organoids, or a combination thereof.

[0034] Kovacs et al. (in: Transcriptomic datasets of cancer patients treated with immune-checkpoint inhibitors: a systematic review. Journal of Translational Medicine 20.1 (2022): 1-15) describe a transcriptome analysis, one sub-aspect of the present responsome analysis, of cancer patients treated with immune-checkpoint inhibitors for providing guidelines of using such type of treatment. In addition, Baia et al. (in: Mining the cancer immuno-responsome: The identification of functional antitumor antibodies from patients receiving checkpoint inhibitors." Cancer Research IS .13 Supplement (2018): 3966-3966) identified an antibody isolated from a patient with non-progressing metastatic cancer treated with checkpoint immunotherapy as a potential therapeutic by analyzing a responsome profile of syngeneic mouse tumor models. The responsome analysis as described is conducted either directly in vivo, such as in human patients or in animal models, or in vitro, such as in artificial cell lines, and therefore lacks the possibility for high-throughput drug screening to generate personalized treatment outcome predictions, a shortcoming that is overcome with the advantageous embodiments of the present invention. Furthermore, the 3D microtissues, in particular the 3D microtumors or organoids of the array as used in the context of the invention resemble the in vivo condition(s), this cannot be replicated using only cell lines.

[0035] In a preferred embodiment of the method according to the present invention, the responsome profile data to be collected is defined as the collective cohort of all or all substantial measurable physiological responses that can be influenced by a certain therapeutic treatment using a drug or drugs or combinations thereof.

[0036] According to the present invention as described above, the responsome profile to be collected in the context of the present invention must comprise two categories of parameters as follows. The first category of the non-disruptive kinetic-based functional analysis may include indicators of the survival state of the patient-derived tissue sample; typically, these are physical parameters, such as the cellular composition, tumor and / or cell size determination, growth kinetics, certain biomarker levels, like LDH release, which at least substantially directly reflect the efficacy of the pharmaceutical drugs or drug combinations applied.

[0037] The second category of the molecular level parameter-based analysis may include indicators of molecular level characteristics that detect the underlying biological state of the cell and / or tissue sample of interest. This includes, for example, the transcriptome, the proteome, and the metabolome. Another preferred example is a respective assay involving a spatial biology assay, as described below.

[0038] Preferred is the method according to the present invention, wherein determining the responsome profile further comprises at least one analysis selected from the group consisting of i) for the non-disruptive kinetic-based functional analysis: cell count, size determination of the 3D microtumor or organoid, determination of released biomarkers such as lactate dehydrogenase (LDH) in the 3D microtumor or organoid culture, and determination of the level and / or expression of pre-selected biomarkers in the 3D microtumor or organoid, determination of the intracellular calcium level, determination of physiological parameters that correlate with the absolute number of cells, the cellular proliferation rate, and the metabolic rate of the cells, wherein preferably the size determination of said 3D microtumor or organoid comprises at least one parameter selected from diameter, perimeter, volume, and area of optical cross section, and wherein preferably the size determination of said 3D microtumor or organoid comprises the use of an imaging device, and optionally further comprises the analysis of growth-kinetics, and ii) for the molecular level parameter analysis; gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as, for example, PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch-repair deficiency (dMMR).

[0039] Another key advantage of the method according to present invention is the fast detection and subsequent analysis of a comprehensive responsome profile containing both aforementioned categories of physiological indicators in order to ensure that the effect / efficacy of the treatment using the drug or combination of drugs can be correlated to respective changes in the said patient-derived cell and / or tissue sample.

[0040] In a preferred embodiment of the method according to the present invention, at least one direct indicator of the survival state of the patient-derived cell and / or tissue sample, in particular the microtumor sample, is determined. In the context of the present invention, the survival state preferably refers to the absolute number of cells present in the said patient-derived cell and / or tissue sample, the proliferation rate of the said patient-derived cells, or the metabolism rate in the said patient-derived sample.

[0041] In a preferred embodiment of the method according to the present invention, the size determination of said patient-derived samples comprises at least one parameter selected from diameter, perimeter, volume, and area of optical cross section, and wherein preferably said size determination of said patient-derived samples comprises the use of an imaging device, and optionally further comprises the analysis of growth-kinetics.

[0042] In a preferred embodiment of the method according to the present invention, preferred is a detecting of pre-selected biomarker level(s) as survival state indicator(s) in the patient-derived tissue and / or cell sample, wherein at least one of the biomarkers indicating the survival state of the patient-derived sample may be released biomarker levels such as LDH. The person skilled in the art is aware of different methods for determining these biomarker levels. For example, the LDH level may be determined using optical methods, such as by luciferase assay or spectroscopic methods like nuclear magnetic resonance (NMR), and the intracellular calcium level may be determined by fluorescence assay, such as the use of chemical fluorescent probes.

[0043] In a preferred embodiment of the method according to the present invention, the at least one molecular level indicator that reflects the cellular or molecular state of the said cells and / or tissues of patient-derived sample are determined. In one example, the cellular or molecular state may be defined as the cellular and / or molecular composition of a cell and / or a tissue patient- derived sample at a given time point. Specific examples include the determination of the transcriptome, the proteome, the metabolome, and the cellular composition of the patient- derived sample(s) comprising, for example, the use of various imaging, spectroscopy, staining, and sequencing methods, as known to the person of skill in the art.

[0044] Preferred is a method according to the invention, wherein the transcriptome can be determined in the patient-derived sample(s) by sequencing, and if preferred, quantifying, the overall mRNA composition in the cell(s) of the patient-derived sample as a molecular level indicator. Examples of the sequencing may comprise:

[0045] 1. mRNA library preparation, where the total or a selected number of all mRNAs may be extracted or isolated from the said patient-derived sample and subsequently may be subjected to reverse transcription so that a corresponding cDNA library is generated,

[0046] 2. sequencing of the corresponding cDNA library, using a method such as next generation sequencing, and

[0047] 3. sequencing data processing, the sequences of the mRNAs in the library may be generated from the cDNA sequences by biochemical principles of reverse transcription, and additional quality control parameters such as number of reads, GC content and read duplicate may be checked for filtering of bad quality data.

[0048] In another preferred embodiment of the method according to the present invention, mRNA sequencing may be used to quantify the amount of a certain mRNA, for example on a single cell basis, so that mRNAs in each cell or each cell type of the 3D microtissues can be sequenced individually, in particular when combined with cell sorting techniques such as fluorescent activated cell sorting (FACS).

[0049] In a preferred embodiment of the method according to the present invention, the proteome can be detected in the patient-derived samples by analyzing the protein composition thereof. This analysis may also provide information on the amino acid sequence, and in some cases, the type of post-translational modifications, of each protein present in the sample(s). An example of the method to determine the proteome, specifically an example using mass spectrometry to determine the proteome, comprises the following steps: total protein extraction from patient- derived samples, protein digestion into peptides using sequence-specific protease, optional enrichment of post-translational modification-containing peptides with the appropriate surface chemistry, peptide separation by high-performance liquid chromatography (HPLC), and analysis by a time-of- flight (TOF) mass spectrometer.

[0050] In a preferred embodiment of the method according to the present invention, the metabolome can be determined in the patient-derived samples, by analyzing a set of small molecule chemical substances and / or other organic compounds that are the product(s) of the metabolism in the cell(s) and / or sample. The metabolome analysis can be achieved by using techniques like mass spectrometry (MS) and / or nuclear magnetic resonance (NMR) spectroscopy.

[0051] In a preferred embodiment of the method according to the present invention, the cellular composition can be determined in the patient-derived tissue samples by analyzing the types of cells in the patient-derived tissue sample(s). Exemplary methods for such analysis include histological staining and cytometry.

[0052] In a preferred embodiment of the method according to the present invention, preselected biomarkers may be determined in the patient-derived sample, for example as additional molecular level indicators, such as PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), or mismatch-repair deficiency (dMMR).

[0053] Specific examples for the responsome analysis according to the present invention include the size determination as a survival state indicator combined with the transcriptome measurement as a molecular level indicator; the size determination as a survival state indicator combined with the transcriptome and / or the genome measurement as molecular level indicator; or the size determination and LDH release level survival state indicator, combined with the transcriptome and cell composition measurements as molecular level indicator(s).

[0054] In the context of the present invention, the term “specific” relates to a drug or drug combination that is linked - at least substantially - to a particular responsome profile and / or to an effect on at least one proliferative disease.

[0055] In the context of the present invention, the term “effective” relates to a drug or drug combination that provides an - at least substantially - beneficial effect on at least one proliferative disease, i.e. mediates and / or causes an improvement on the clinical phenotype of the disease.

[0056] In the context of the present invention, the terms “contacting” relates to the provision of an interaction of a tumor tissue sample, such as a microtumor or organoid, with a drug or drug combination as tested according to the present invention. Contacting can be sequentially or simultaneous, as described herein, and for preselected times as indicated. In some instances, as discussed herein, contacting is referred to a “treating” or “treatment”.

[0057] In the context of the present invention, the term drug may also include a substance, that supports, boosts, or enables the pharmaceutical effect of one or several other drugs and that may not have a direct pharmaceutical effect on its own.

[0058] In the context of the present invention, the terms “personalized” relates to relates to a drug or drug combination that is linked - at least substantially - to a responsome profile and / or to an effect on at least one proliferative disease of an individual subject or patient.

[0059] In the context of the present invention, the terms “suitable culture medium” relates to any cell culture medium that can be used in order to cultivate proliferative disease tissue samples in order to generate microtissues and microtumors or organoids, as described also further below.

[0060] As mentioned above, several physiological parameters can be determined in the context of generating the responsome according to the present invention, in order to identify improved antiproliferative, and preferably patient-specific, drugs and / or combinations of drugs. In the method according to the present invention, wherein the identifying in step d) is based on the responsome profile as determined in step c), a preferred drug or a combination of drugs may be identified, if: i) according to the non-disruptive kinetic-based functional analysis, it substantially inhibits the growth of the 3D microtissue, in particular the 3D microtumor or organoid of the array, as determined by a reduction in tissue size, a slower growth kinetics, and / or an LDH release, and ii) according to the molecular level parameter analysis, the tissue does not show resistance against the drug or combination of drugs, as, for example, determined by a mutation in specific genes that are targeted by the drug or combination of drugs, a lower expression level of the products of the genes that are targeted by the drug or combination of drugs is found, and / or the absence of drug-resistance inducing mutations is found.

[0061] In one preferred aspect, the method according to the present invention comprises or further comprises at least one spatial biology assay in order to evaluate the spatial context of properties of cells within the tissue(s), and wherein said identifying is furthermore based to at least in part on the on the data as generated in said at least one spatial biology assay.

[0062] In the context of the present invention, spatial biology assays in order to evaluate the spatial context of cells within a given tissue is an extremely valuable tool to further understand tumor biology. The value of the data as generated in the method according to the present invention is tremendously enhanced by the addition of the spatial functional information as generated, which will provide information on the effects of specific drugs and combinations of drugs and their impact on spatial cell rearrangements and which groups of cells are being affected.

[0063] Spatial profiling assays in general characterize tissue organization and three-dimensional architecture. For the methods according to the present invention, preferred examples for spatial biology assays for cells and / or the 3D microtissues, in particular the 3D microtumors or organoids of the array according to the present invention include two well-established molecular biology techniques, immunofluorescence and next-generation sequencing. Using both technologies together, the present method can ascertain how transcriptional dynamics vary within a spatial context. Spatial information can be obtained at various scales, including at the tissue, single cell, and subcellular levels (see also Method of the Year 2020: spatially resolved transcriptomics. Nat Methods 18, 1 (2021). https: / / doi.org / 10.1038 / s41592-020-01042-x). Further preferred methods can be found in, for example, Guilliams et al. (in: Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell. 2022 Jan 20;185(2):379-396.e38. doi: 10.1016 / j.cell.2021.12.018. Epub 2022 Jan 11. PMID: 35021063; PMCID: PMC8809252). The spatial analysis may further involve computational methods (see, for example, Fleck JS, et al. Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases. Cell Stem Cell. 2021 Jun 3;28(6): 1148- 1159. e8. doi: 10.1016 / j.stem.2021.02.015. Epub 2021 Mar 11, Wahle, P., Brancati, G., Harmel, C. et al. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nat Biotechnol (2023). https: / / doi.org / 10.1038 / s41587-023-01747-2).

[0064] In another preferred embodiment of the method according to the present invention steps a) to d) can be repeated for at least once (or 2, 3, 4 or more times). In this aspect, the method comprises combining the drugs as identified in the first round of the method. Because of these rounds of selection, for the combinations of drugs the most effective ones are identified and can be selected for the patient selective treatment. It is expected that the method will allow to identify new improved and even synergistic drug combinations, which will allow, for example, to reduce the doses of drugs, and avoid resistance development.

[0065] Once a drug or combination of drugs has been identified as specific and effective the method according to the present invention may further comprise the step of e) selecting a drug or combination of drugs as specific and effective, and in particular personalized, against a proliferative disease in a subject based on the identifying in step d), be it in a single cycle method, or repeated method as described above.

[0066] In one preferred aspect, the method according to the present invention comprises a selection of the drug or combination of drugs based in the identification as described herein, wherein the selection is based on a combination of the responsome analysis as above and additionally the at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs as identified and / or provided by a database. In case where, for example, several drugs and / or drug combinations have been identified as possibly effective, the inclusion of the information about genes and / or proteins of a pathway as involved in the effect of the drug and / or combination of drugs may be used to select a specific and more effective drug or combination of drugs for treatment. This may increase the effect, reduce the likelihood of resistance, and may reduce any side-effects for the patient.

[0067] In principle, any known anti-proliferative drug or combination of drugs can be used in the context of the present invention, nevertheless, the method can also be used in order to identify novel therapeutics. Preferred is the method according to the present invention, wherein the drug is selected from the group consisting of anti-cancer drugs, such as, for example, alkylating agents, antimetabolites, natural products, hormones, tyrosine inhibitors, chemotherapeutic compounds, anti-cancer antibodies, in particular gemcitabine, abraxame, trametinib, olaparib, oxaliplatin, erlotinib, 5-FU, docetaxel, and pemetrexed, small molecule drugs, chemotherapeutic anti-cancer drugs, proteinaceous drugs, antibodies and genetically engineered variants thereof, nucleic acid drugs, antibody-drug-conjugates, and genetically engineered cells, such as, for example, CAR T-cells.

[0068] In another aspect of the method according to the present invention, the drug or combination of drugs which the 3D microtissues are contacted with in step b) are selected from the group consisting of cytotoxic, cytostatic and / or chemotherapeutic agents, targeted drugs, immunotherapeutic agents, small molecules from screening libraries, and / or combinations thereof. Examples for cytotoxic, cytostatic and / or chemotherapeutic agents are anastrozole, azathioprine, beg, bicalutamide, chloramphenicol, cyclosporin, cidofovir, coal tar containing products, colchicine, danazol, diethylstilbestrol, dinoprostone, dithranol containing products, dutasteride, estradiol, exemestane, finasteride, flutamide, ganciclovir, gonadotrophin, chorionic, goserelin, interferon containing products (including peg-interferon), leflunomide, letrozole, leuprorelin acetate, medroxyprogesterone, megestrol, menotropins, mifepristone, mycophenolate mofetil, nafarelin, estrogen containing products, oxytocin (including syntocinon and syntometrine), podophyllyn, progesterone containing products, raloxifene, ribavirin, sirolimus, streptozocin, tacrolimus, tamoxifen, testosterone, thalidomide, toremifene, trifluridine, triptorelin, valganciclovir, and zidovudine. Targeted drugs are medications that increase in concentration in some parts of the body relative to others, such as antibodies. Examples are in brain cancer: bevacizumab, everolimus; breast cancer: bevacizumab, everolimus, lapatinib, pertuzumab, trastuzumab and its antibody drug conjugates; in colorectal cancer: aflibercept, bevacizumab, cetuximab, panitumumab, regorafenib, and dermatofibrosarcoma protuberans: imatinib. Immunotherapeutic agents are used in immunotherapy that is a form of cancer treatment that uses the power of the body's immune system to prevent, control, and eliminate cancer. Examples are monoclonal antibodies to treat cancer, CAR T-cell therapy, immune checkpoint inhibitors to treat cancer, cancer vaccines, immunomodulating drugs (IMiDs), and cytokines. Immune-check point inhibitor (ICI) are inhibitors of the so-call immune checkpoints which, in physiological situations, play a role in preventing exaggerated immune response, but could also be used by cancer cells to suppress immune responses which could otherwise induce cancer cell death. Examples are anti-CTLA- 4 antibodies which inhibits cytotoxic T-lymphocyte associated protein (CTLA-4), such as ipilimumab, anti-PD-1 antibodies which inhibits programmed cell death 1 receptor (PD-1), such as pembrolizumab, nivolumab, or sintilimab, and anti-PD-Ll antibodies which inhibit programmed cell death ligand 1 (PD-L1), such as atezolizumab. Further examples are natural product libraries which are made up of compounds derived from natural sources, synthetic compound library which are made up of compound synthesized in the labs, and fragment library which are made up of small, low-molecular- weight compounds. Examples for antibody drug conjugates are belantamab mafodotin, brentuximab vedotin, enfortumab vedotin, gemtuzumab ozogamicin, inotuzumab ozogamicin, loncastuximab tesirine, mirvetuximab soravtansine, polatuzumab vedotin, sacituzumab govitecan, tisotumab vedotin, trastuzumab deruxtecan, trastuzumab emtansine, or valemetostat tosilat. Examples for nucleic acid drugs are custirsen, aprinocarsen, imetelstat or oblimersen.

[0069] Preferred is the method according to the present invention, wherein the combination of drugs comprises at least two drugs that are applied jointly or sequentially. While the combination of 2 drugs is preferred, also combinations of 3 and more, 4 and more or 5 and more are included in the present invention.

[0070] The drugs or combination of drugs that are used in the assays according to the present invention can be tested at any suitable concentration, preferably at concentrations that are commonly used in the treatment of proliferative diseases. Further preferred is the method according to the present invention, wherein the 3D microtissues, in particular 3D microtumors or organoids of the array are contacted with the drug or combination of drugs at the Cmax concentration(s). Other concentrations may be within a physiological range such as Cmax to Ctrough, i.e. the lowest concentration reached by a drug before the next dose is administered, if known.

[0071] The drugs or combination of drugs that are used in the assays according to the present invention can be contacted with the drugs or combination of drugs that are used in the assays according to the present invention for any time period(s) and again preferably for time periods that are commonly used in the treatment of proliferative diseases. Further preferred is the method according to the present invention, wherein the drugs or combination of drugs is / are contacted with the 3D microtissues, in particular 3D microtumors or organoids of the array for a time span depending at least in part on their t value(s). Another aspect of the method according to the current invention relates to the determination of the data for the responsome analysis at several time points. Sampling is preferably done with primary subject or patent biopsy materials both before and after treatment with a drug or a combination of drugs, and / or with the patient-derived array of 3D microtissues both before and after treatment with one drug or a combination of drugs. Depending on the parameters to be determined as the data for the responsome analysis, sampling may be done at least once, but preferably at all aforementioned sampling points.

[0072] One of the particular advantages of the present invention lies in the fact that the subject-derived sample is used in order to produce proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids that are then used in the testing according to the invention. This leads to a substantial conservation of the in vivo situation, in particular the cellular composition and diversity as present in the subject when testing the drug or combination of drugs. Therefore, preferred is a method wherein before the contacting in step b) the primary proliferative disease tissue samples or the proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, comprise cells reflecting the composition of the major cell types of the native tumor tissue, such as, for example, connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells, and CD8+ T lymphocytes.

[0073] Nevertheless, the the subject-derived sample can also be a sample of a secondary proliferative disease tissue (e.g., metastatic tissue), subject deriver xenografts (PDX-tissue), and patient- derived cell lines. For comparative purposes, “classical” cell lines may be used as well.

[0074] Because of the substantial conservation or maintenance of the in vivo situation, in particular the cellular composition and diversity as present in the subject, the relevance of the data as obtained, and subsequently the responsome is improved compared to, for example, a method using cell lines as the source for the testing of drugs or combination of drugs.

[0075] Preferred is the method according to the present invention, wherein the subject is a mammal, such as, for example, a human, patient, and wherein preferably said patient suffers from, or is being diagnosed for, a neoplastic disease or tumour. Examples are a proliferative disease that is selected from neoplastic diseases, benign proliferative diseases, malign proliferative diseases, psoriasis, cancer, such as, for example, lung cancer, cervical cancer, brain cancer, gastric cancer, liver cancer, bone cancer, head and neck cancer, benign tumours, adenomas, endometriosis, hyperplasia (e.g. prostate), thyroid nodules, haemangioma, lymphangioma, keloids, warts, and colon cancer.

[0076] In the context of the present invention, any suitable sample from a subject having or suspected of having a proliferative disease may be used. Preferred is a method according to the present invention, wherein said patient-derived tissue sample is selected from a sub-sample derived from a primary tissue sample, a primary tumour sample, and a metastasis sample, and wherein preferably said tissue sample has been obtained by a method comprising core biopsy, tumor resection, liquid biopsy and / or needle aspiration, and / or wherein said tissue sample and / or the dissociated cells are frozen and re-thawed prior to the generation of said 3D microtissues. It is preferred to obtain a sample that reflects or substantially reflects the cellular composition and diversity as found or present in the diseased tissue before generating the microtumor or organoid and the testing of the drug or combination of drugs.

[0077] The present invention generally relies on responsome data generated from subject or patient- derived tissue samples, particularly patient-derived 3D microtissues. In the present invention, “subject-derived” materials preferably refers to any cell and / or tissue sample from patient biopsies or any cell and / or tissue samples generated ex vivo that contain cells and / or tissues obtained from subject or patient biopsies.

[0078] In this context, a 3D microtissue refers to an in vitro generated cell aggregate comprising any selected cell types, and a patient-derived 3D microtissue or organoid then refers to a 3D microtissue containing, at least in part, cells and / or tissues from patient biopsies. Consequently, a patient-derived 3D microtumor, which is itself a type of 3D microtissue, is a cell aggregate that contains, at least in part, cancer cells from subject or patient biopsies. In one preferred aspect of the invention, the addition of other cell types during the generation of the 3D microtissues is possible, particularly immune cells that could potentially infiltrate and interact with the cancer cells, or other stromal cells such as blood vessel forming endothelial cells. The person of skill is aware of method for generating these 3D microtissues from patient-derived biopsy samples, and WO 2021 / 110799A2 for example teaches protocols for generating 3D microtissues, particularly 3D microtumors and organoids from subject or patient-derived materials.

[0079] In general, the method according to the present invention is performed in culturing vessels that allow for the formation of 3D microtissues from subject or patient-derived biopsy samples. In view of the necessity to generate a responsome profile when testing the drug or combination of drugs, preferred are plates comprising a multi-well format, wherein such a plate comprises at least 5, at least 16, or at least 32 wells. Of course, several plates may be used in parallel. In the context of the present invention, an “array” relates to a set of separate subject or patient-derived tissue samples that are analyzed in parallel. An array of subject or patient-derived tissue samples may be provided in the form of separate tissue samples cultured in multi-well plates. The array then may comprise any suitable number of separate tissue samples as described above and herein, but preferably comprises at least equal to or more than the number of drugs and / or drug combinations to be tested. More preferably, the number is equal to or higher than the product of the number of drugs and / or combinations of drugs to be tested and the number of parameters to be measured as the responsome profile. The array may comprise at least 5 or more, or 6, 7, 8, 9, 10 or more, 12, 48, 96, 128, 384 or more, or even 200, 300, 400, or more subject or patient- derived tissues.

[0080] As part of the method according to the present invention, microtumors or microtissues may be produced from primary tissue. Preferred is a method according to the present invention, wherein the providing of said 3D microtissues, in particular 3D microtumors or organoids comprises a maturation time of about 6 hours to 7 days, preferably about 1 to 6 days, more preferably about 2 to 5 days. This will allow the 3D microtissues to grow into an acceptable size in order to perform the method according to the present invention, such as, for example, 3D microtumors as generated having a size of 350 pm + / - 100 pm, in particular a consistent size.

[0081] Another one of the particular advantages of the present invention lies in the fact that the present method is fast in order to provide a meaningful responsome analysis from a subject-derived sample. As mentioned above, time is of the essence when wanting to efficiently treat a patient suffering from a proliferative disease, in particular cancer. In a preferred method according to the present invention steps a) to d), and optionally a) to e), are performed within about 1 to 7 days, more preferably about 3 to 6 days.

[0082] In another important aspect of the method according to the present invention, the method further comprises the step of identifying genes and / or proteins of a cellular drug-specific pathway as new drug targets. This aspect of the invention benefits from the responsome profile analysis according to the present invention, that allows to identify new cellular drug-specific pathways that are changed or modified during the analysis of the effects of the drug or combination of drugs in the context of the present invention.

[0083] Therefore, preferred is the method according to the present invention, further comprising at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs.

[0084] Another important aspect of the method according to the present invention relates to the use of computer programs and databases when generating and analyzing the responsome-profile(s) according to the present invention. Preferred is therefore the method according to the present invention, wherein the responsome profile as identified is stored on a centralized server, and / or the identifying comprises at least one of i) comparing the responsome profile with a responsome profile generated from tissue samples of a healthy subject, ii) comparing the responsome profile with a responsome profile generated from tissue samples of other subjects diagnosed with a proliferative disease, treated or untreated.

[0085] Therefore, in another preferred embodiment of the method according to the current invention, an improved or even optimal therapy is predicted not only using the responsome profile data and / or analysis data as generated from one sample of a patient, but also, with the over-time accumulation of the responsome data from a number of individuals, for groups of patients sharing the same or similar proliferative disease feature(s). This number of individuals may be 50, 100, 200, 500, or 1000 or more subjects with known proliferative diseases or conditions.

[0086] In another preferred embodiment of the method according to the present invention, the changes as identified in a (one) specific (e.g. personalized) responsome profile may be compared or integrated otherwise across responsome data acquired from different sources. Such comparison can particularly be between a patient and another one or a group of healthy individuals, between a patient and another one or a group of patients suffering from or being diagnosed for the same type of disease, particularly the same type of proliferative disease, tumor, or neoplastic disease.

[0087] The responsome-profile comparison between a subject or patient and one or a group of healthy individuals can be used both as a strategy to identify potential side effects of a drug or a combination of drugs, or to determine an underlying molecular mechanism and / or cellular pathway of the proliferative disease. Similarly, the comparison of the responsome profile between a patient and another patient or a group of patients suffering from or being diagnosed for the same type of proliferative disease can be used both as a way to identify a drug or combination of drugs that is / are effective for all individuals as examined and their proliferative disease, or as a method to understand why a specific anti-proliferative treatment may not be effective in certain subjects or patients, although they are - allegedly - diagnosed for the same type of proliferative disease (for example, an underlying cellular mechanism causing resistance in certain scenarios.

[0088] Yet another advantage associated with the method according to the present invention is the possibility of implementing machine learning algorithms. Preferred is therefore a method according to the present invention, wherein said identifying comprises a method using a deep learning algorithm Preferably, the algorithm is trained using the responsome profiles as generated and / or stored.

[0089] The machine learning algorithms are helpful in finding the optimal drug or combination of drugs, in predicting efficient treatments, and / or the identification of novel therapeutics concepts, especially for cancer patients. The use of machine learning algorithms has greatly boosted the precision for cancer therapy prediction in recent year. However, despite a larger number of machine learning (ML) algorithms as available, for example Rafique et. al. (in: "Machine learning in the prediction of cancer therapy." Computational and Structural Biotechnology Journal 19 (2021): 4003-4017) identified that the predictive power of ML algorithm is heavily limited by the amount of primary patient data, as most ML algorithms are trained and developed using cell line data. In addition, the data curation from various clinical data sources is another challenge faced by ML algorithm development due to difficulties in incorporating non-uniform data formats. The responsome analysis with patient-derived tissues, particularly with patient-derived 3D microtissues addresses both these issues by generating primary patient sample data on all levels with little sample quantity and providing sufficient and easily accessible datasets for ML algorithm training and development.

[0090] Another aspect of the present invention is the prediction of the treatment outcome by implementing an algorithm, preferably a machine-learning based algorithm, that is trained on the responsome data as collected and stored in an accessible database. The machine learning algorithm may be a deep learning algorithm that uses advanced machine learning techniques such as deep neural networks or ensemble learning in order to process the subject- or patient- derived responsome data and then for example predicts the treatment efficacy of identified drugs or combinations of drugs. The deep neural networks allow the algorithm to be trained on complex relationships between patient data and treatment outcomes, while the ensemble learning ensures that the algorithm is robust to noisy or incomplete data.

[0091] In a preferred embodiment of the method according to the current invention, said machine learning algorithm is a supervised learning algorithm, wherein the collected responsome data comprises the dataset for training the algorithm. These inputs can be supplemented with other data, such as genomic data of patients, such as mutations / biomarkers, patient parameters (physiology, chemistry), patient-characteristics (age, gender, ethnics, etc.), and prior therapies and responses.

[0092] Another advantage of the method according to the present invention is the cross-validation of the accuracy of the prediction by said machine learning algorithm with additional responsome data intentionally withheld from being used as the training dataset. This is only achievable with the current invention, since the additional validation relies on an excess of input data, which is made available through the generation of array of microtissues from patient-derived samples.

[0093] Another aspect of the present invention then relates to a method for identifying adverse effects associated with a treatment with a patient-specific drug or combination of drugs in a subject, comprising performing the method according to the present invention, further comprising the step of testing and analyzing said subject-specific drug or combination of drugs for adverse effects in said patient. In this aspect, the present method ultimately allows to generate a risk profile for the patient and the effect of the treatments as tested, where the benefits of a drug or drug composition may be compared and balanced with prospective side-effects.

[0094] Similarly to the above, another aspect of the present invention then relates to a method for stratifying a patient with respect to a treatment with a patient-specific drug or combination of drugs, comprising performing the method according to the present invention, comprising identifying at least one subject-specific drug or combination of drugs based on the responsome profile, and optionally further based on the molecular pathway, as determined, and stratifying of said subject based on said subject-specific drug or combination of drugs as identified. In this aspect, the patient may be grouped into treatment group(s) that is treated with a specific according to the present invention and for which this treatment provides the biggest benefit or even the lowest risk of side-effects.

[0095] Another aspect of the present invention the relates to a method for monitoring the treatment of a proliferative disease with a subject-specific drug or combination of drugs, comprising performing the method according to the present invention, treating said patient with said at least one patient-specific drug or combination of drugs as identified, and repeating the method according to the present invention on a sample of said subject following the treatment, and optionally adjusting the treatment based on the result(s) of said repeating.

[0096] Preferred is a method according to the current invention, wherein said changes in the responsome profile as detected may be analyzed by comparing samples that have been collected at different sampling points in time with other samples derived from the same patient. This analysis may comprise the steps of i) raw responsome data collection, wherein pre-selected physiological parameters in the responsome profile are determined according to the method of the present invention, and are added to a centralized server for storage thereof, ii) data processing, wherein the raw data may be subject to annotation, clustering, sorting, reformatting, compression, and the like, iii) data input into analysis software, wherein the processed raw data may be fed into at least one algorithm, for example implemented by an automated computer software, whereby a preferred drug or drug combination may be identified, and iv) postprocessing of the before responsome data analysis for evaluating the validity of the drug or combination of drugs as determined.

[0097] As mentioned above, preferred is the method according to the present invention, wherein said step of identifying and / or selecting includes the use of additional clinical parameters, such as patient specific data and / or treatment history.

[0098] In a preferred embodiment of the method according to the invention, the transcriptome changes in the responsome profile are analyzed in order to interpret the effect of the treatment as applied on the gene expression of said subject or patient-derived cell and / or tissue samples. A specific treatment (drug or combination of drugs), in the method according to the present invention, may be preferred and selected as a suitable treatment for the patient, if the treatment either upregulates genes generally known to be related to the survival of the diseased tissues and / or cells, for example known to downregulate genes generally related to the support of a survival of the diseased tissues and / or cells. The person skilled in the art is aware of various genes whose expression level is correlated to cell survival and unregulated proliferation. Examples of genes whose expression levels are generally related to the suppression of survival of neoplastic cells are genes involved in cell proliferation such as KRAS, BRAF, MYC, EGFR, HER2, genes involved in DNA repair mechanism such as TP53, genes involved in cell growth, division and differentiation such as PIK3CA, PTEM, NF1, cell cycle related genes such as CDKN2A, and apoptosis related genes such as BCL2.

[0099] In a preferred embodiment of the method according to the present invention, the proteome changes in the responsome are analyzed in order to determine the effect of the treatment as applied on the protein composition of said patient-derived cell and / or tissue samples. In the method according to the current invention, a specific treatment (drug or combination of drugs) is regarded preferred as the selected treatment for the said patient, if said treatment either upregulates proteins generally related to suppression of survival of the diseased tissues and / or cells or depletes proteins generally related to support of survival of the diseased tissues and / or cells. The person skilled in the art is aware of various proteins that are correlated to cell survival and unregulated proliferation. Examples of proteins crucial for the survival and growth of neoplastic cells and / or tissue are proteins involved in apoptosis such as Bcl-2, Akt, nuclear factor kappa B (NF-KB), cMyc, murine double minute 2 (MDM2), p53, survivin and phosphatidylinositol 3-kinase (PI3K), proteins involved in angiogenesis and / or glycolysis, such as hypoxia-inducible factor 1 alpha (HIF-la) and vascular endothelial growth factor (VEGF), and proteins involved in signaling processes for cell survival such as human epidermal growth factor receptor 2 (HER2), focal adhesion kinase (FAK) (focal adhesion kinase) and heat shock protein 90 (Hsp90).

[0100] In a preferred embodiment of the method according to the present invention, the metabolome changes in the responsome are analyzed for interpreting the effect of the applied treatment on the metabolism of the patient-derived cell and / or tissue samples. Neoplastic cells may have a significantly altered metabolism. As a specific example, some neoplastic tissue will have an elevated level of glycolysis and is thus enriched in glucose. In the analysis of the responsome profile for determining the best therapy, the metabolome may serve as a special criterion, wherein a certain therapy is preferred, if it induces metabolomic changes in the patient-derived cells and / or tissues for them to resemble the metabolome found in healthy cells and / or tissue. In a preferred embodiment of the method according to the present invention, the cell composition changes and / or morphologic changes are analyzed for interpreting the effect of a specific treatment on the protein composition of the subject or patient-derived cell and / or tissue samples. Other cell types that coexist in the same micro-environment may interact with the said patient-derived diseased cells and / or tissues. The interactions between different cell types in the tumor microenvironment can have both pro- and anti-tumor effects. Examples of cell types that may facilitate the survival of cancer cells include fibroblasts which can produce an abnormal extracellular matrix or growth factors that promotes cancer cell survival and invasion, endothelial cells, which are cells that line blood vessels to provide the cancer cells with nutrients and oxygen to support their growth and cancer-associated adipocytes (CAAs) which are adipocytes that accumulate in the tumor microenvironment and can secrete growth factors to promote cancer cell survival. Examples of cell types that may impede the survival of cancer cells include some immune cells such as cells and natural killer cells which can recognize and attack cancer cells. Particularly, engineered immune cells such as CAR T-cells which can be added to the patient-derived tissue sample as a target therapy may also be identified and characterized through cell composition analysis of the said cells and / or tissue sample. In the method according to the present invention, a therapy is preferred if it reduces the occurrence of pro-cancer cell types or if it increases the occurrence of the anti-cancer cell types.

[0101] In a preferred embodiment of the method according to the present invention, the changes in preselected molecular level biomarkers in the responsome data are analyzed for determining the effect of the applied treatment on the said patient-derived cell and / or tissue samples. Particularly, some molecular level markers can be an indication of the suitability of some therapeutic method for the said patient. For example, the expression level of PD-L1 (program death-ligand 1) can be detected and used as an indicator for the efficacy of immune checkpoint- inhibitor-based therapy. Another example for a preselected biomarker is tumor mutational burden (TMB), which is an indicator of the applicability of cancer immunotherapy.

[0102] In a preferred embodiment of the method according to the present invention, the method is at least in part, and preferably fully, automated, comprising the use of at least one robot and / or computing apparatus, as, for example, described above. Preferred is a method according to the present invention, wherein a large variety of drugs or combinations of drugs are tested on the said array of the patient-derived 3D microtissues. Further details regarding a preferred device and method for a systematic, high-throughput and automated screening of drugs or drug combinations in human-derived tissues samples, particularly 3D microtissues, are disclosed, for example, in WO 2020 / 104549 (incorporated herein by reference) and is known to the skilled person.

[0103] Another aspect of the present invention then relates to a computer program for performing the method according to the present invention on a suitable computer, as, for example, described above. Preferred is the computer program according to the present invention, wherein the machine learning algorithm comprises a deep learning algorithm, for example a deep neural networks algorithm, also as described herein.

[0104] Another aspect of the present invention then relates to a drug or combination of drugs for use in the prevention or treatment of a proliferative disease, wherein said treatment comprises performing the method according to the present invention, preferably further comprising the step of providing a subject-specific and / or personalized prevention or treatment. Preferred is the drug or combination of drugs for use according to the present invention, wherein the neoplastic disease or tumor is a drug-resistant neoplastic disease or tumor as described above.

[0105] According to the invention, the term “personalized prevention” or “personalized treatment” shall refer to the medical model that separates patients or subjects into different groups — with medical decisions, practices, interventions and / or products being tailored to the individual patient or subject based on their predicted response or risk of disease, preferably based on the method as described herein.

[0106] Another aspect of the present invention then relates to a testing system comprising means for performing the method according to the present invention, comprising a) a unit for culturing an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample derived from the subject as provided, b) a drug testing unit for contacting said array of said 3D microtissues with at least one drugs or combination of drugs to be tested, c) a unit for determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, comprising i) a first analysis unit for a non- disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, ii) a second analysis unit for determining a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined. Preferred is the testing system according to the present invention, further comprising at least one database for collecting and / or storing data regarding the responsome profile as identified, and optionally additional data with respect to the preselection and / or grouping of the combination of drugs, physiological parameters, data with respect to the effect(s) of said drugs or combination of drugs thereof, data with respect to the cellular and / or tissue pathway-specific responsome analysis, data with respect to adverse effects, data with respect to additional clinical parameters, such as patient specific data and / or treatment history, data with respect to the stratification and / or monitoring, and data for automatization of the system, for example for controlling at least one robot. Further details regarding a preferred device and method for a systematic, high-throughput and automated screening of drugs or drug combinations in human-derived tissues samples, particularly 3D microtissues, are disclosed, for example, in WO 2020 / 104549 (incorporated herein by reference) and is known to the skilled person.

[0107] Another aspect of the present invention then relates to the use of the testing system according to the present invention or the computer program according to the present invention for identifying at least one subject-specific drug or combination of drugs and / or for at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs according to the method according to the present invention as described above.

[0108] The present invention relates to the following items:

[0109] Item 1. A method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the steps of a) Providing an array of i) primary proliferative disease tissue samples and / or ii) proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, from a subject having or being diagnosed for a proliferative disease in a multi-well format in a suitable culture medium, wherein the multi-well format comprises between 5 wells and a number of wells corresponding to the drugs and / or combinations of drugs and doses thereof to be tested plus controls, b) Contacting the 3D microtissues, in particular 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined amount of time or predefined different amounts of time, c) Determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of absolute number of cells, proliferation rate, growth kinetics, size of cells, cellular composition of the cells, tissues, tumors or organoids, and metabolic rate, and ii) a second step of a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, genetic mutation, and selected biomarker level analysis, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) Identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined in step c).

[0110] Item 2. The method according to Item 1, wherein determining the responsome profile further comprises at least one analysis selected from the group consisting of i) for non-disruptive kinetic-based functional analysis, cell count, size determination of said 3D microtumor or organoid, determination of the LDH release in said 3D microtumor or organoid, and determination of pre-selected biomarkers in said 3D microtumor or organoid, intracellular calcium level, physiological parameters that directly indicate the absolute number of cells, the proliferation rate, and the metabolic rate, wherein preferably said size determination of said 3D microtumor or organoid comprises at least one parameter selected from diameter, perimeter, volume, and area of optical cross section, and wherein preferably said size determination of said 3D microtumor or organoid comprises the use of an imaging device, and optionally further comprising the analysis of growth-kinetics, and ii) for the molecular level parameter analysis, gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as, for example, PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch-repair deficiency (dMMR). Item 3. The method according to Item 1 or 2, wherein the identifying in step d) is based on the responsome profile as determined in step c), wherein a drug or a combination of drugs is identified, if: i) according to the non-disruptive kinetic-based functional analysis, it substantially inhibits the growth of the 3D microtissue, in particular the 3D microtumor or organoid of the array, as determined by a reduction in tissue size, a slower growth kinetics, and / or LDH release, ii) according to the molecular level parameter analysis, the tissue does not show resistance against the drug or combination of drugs, as, for example, determined by a mutation in specific genes that are targeted by the drug or combination of drugs, a lower expression level of the products of the genes that are targeted by the drug or combination of drugs is found, and / or the absence of drug-resistance inducing mutations is found.

[0111] Item 4. The method according to any one of Items 1 to 3, wherein the method further comprises at least one spatial biology assay in order to evaluate the spatial context of cells within the tissue(s), and wherein said identifying is furthermore based to at least in part on the on the data as generated in said at least one spatial biology assay.

[0112] Item 5. The method according to any one of Items 1 to 4, further comprising the step of e) selecting a drug or combination of drugs as specific and effective, and in particular personalized, against a proliferative disease in a subject based on the identifying in step d).

[0113] Item 6. The method according to any one of Items 1 to 5, wherein steps a) to d) are repeated for at least once comprising combining the drugs as identified in the first round of the method.

[0114] Item 7. The method according to any one of Items 1 to 6, wherein the drug is selected from the group consisting of anti-cancer drugs, such as, for example, alkylating agents, antimetabolites, natural products, hormones, tyrosine inhibitors, chemotherapeutic compounds, anti-cancer antibodies, in particular Gemcitabine, Abraxame, Trametinib, Olaparib, Oxaliplatin, Erlotinib, Erlotinib, 5-FU, Docetaxel, and Pemetrexed, small molecule drugs, chemotherapeutic anticancer drugs, proteinaceous drugs, antibodies and genetically engineered variants thereof, nucleic acid drugs, antibody-drug-conjugates, and genetically engineered cells, such as, for example, CAR T-cells.

[0115] Item 8. The method according to any one of Items 1 to 7, wherein the combination of drugs comprises at least two drugs that are applied jointly or sequentially. Item 9. The method according to any one of Items 1 to 8, wherein the 3D microtissues, in particular 3D microtumors or organoids of the array are contacted with the drug or combination of drugs at the C max concentration(s).

[0116] Item 10. The method according to any one of Items 1 to 9, wherein the drugs or combination of drugs is / are contacted with the 3D microtissues, in particular 3D microtumors or organoids of the array for a time span depending at least in part on their t!4 value(s).

[0117] Item 11. The method according to any one of Items 1 to 10, wherein before the contacting in step b) the primary proliferative disease tissue samples or the proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, comprise cells reflecting the composition of the major cell types of the native tumor tissue, such as, for example, connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells, and CD8+ T lymphocytes.

[0118] Item 12. The method according to any one of Items 1 to 11, wherein the subject is a mammal, such as, for example, a human, and wherein preferably said patient suffers from, or is being diagnosed for, a neoplastic disease or tumor.

[0119] Item 13. The method according to any one of Items 1 to 12, wherein the proliferative disease is selected from neoplastic diseases, benign proliferative diseases, malign proliferative diseases, psoriasis, cancer, such as, for example, lung cancer, cervical cancer, brain cancer, gastric cancer, liver cancer, bone cancer, head and neck cancer, benign tumours, adenomas, endometriosis, hyperplasia (e.g. prostate), thyroid nodules, haemangioma, lymphangioma, keloids, warts, and colon cancer.

[0120] Item 14. The method according to any one of Items 1 to 13, wherein the multi-well format comprises a multi-well plate having at least 5, at least 16, or at least 32 wells.

[0121] Item 15. The method according to any one of Items 1 to 14, wherein the providing of said 3D microtissues, in particular 3D microtumors or organoids comprises a maturation time of about 6 hours to 7 days, preferably about 1 to 6 days, more preferably about 2 to 5 days, and / or wherein said 3D microtumors as generated have a size of 100-450 pm, preferably 350 pm + / - 100 pm.

[0122] Item 16. The method according to any one of Items 1 to 15, wherein steps a) to d), and optionally a) to e), are performed within about 1 to 14 days, more preferably about 3 to 12 days.

[0123] Item 17. The method according to any one of Items 1 to 16, further comprising at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs.

[0124] Item 18. The method according to Item 17, further comprising the step of identifying genes and / or proteins of the pathway as drug targets.

[0125] Item 19. The method according to any one of Items 1 to 18, wherein the responsome profile as identified is stored on a centralized server, and / or the identifying comprises at least one of i) comparing the responsome profile with a responsome profile generated from tissue samples of a healthy subject, ii) comparing the responsome profile with a responsome profile generated from tissue samples of other subjects diagnosed with a proliferative disease, treated or untreated.

[0126] Item 20. The method according to Item 19, wherein said identifying comprises a method using a deep learning algorithm, wherein preferably the algorithm is trained using the stored responsome profiles.

[0127] Item 21. A method for identifying adverse effects associated with a treatment with a patientspecific drug or combination of drugs in a subject, comprising performing the method according to any one of Items 1 to 20, further comprising the step of testing and analyzing said subjectspecific drug or combination of drugs for adverse effects in said patient.

[0128] Item 22. A method for stratifying a patient with respect to a treatment with a patient-specific drug or combination of drugs, comprising performing the method according to any one of Items 1 to 20, comprising identifying at least one subject-specific drug or combination of drugs based on the responsome profile, and optionally further based on the molecular pathway, as determined, and stratifying of said subject based on said subject-specific drug or combination of drugs as identified.

[0129] Item 23. A method for monitoring the treatment with a subject-specific drug or combination of drugs, comprising performing the method according to any one of Items 1 to 20, treating said patient with said at least one patient-specific drug or combination of drugs as identified, and repeating the method according to any one of Items 1 to 20 on a sample of said subject following the treatment, and optionally adjusting the treatment based on the result(s) of said repeating.

[0130] Item 24. The method according to any one of Items 1 to 20, wherein said step of identifying and / or selecting includes the use of additional clinical parameters, such as patient specific data and / or treatment history.

[0131] Item 25. The method according to any one of Items 1 to 24, wherein said method is at least in part, and preferably fully, automated, comprising the use of at least one robot and / or computing apparatus.

[0132] Item 26. A computer program for performing the method according to any one of Items 17 to 20 on a suitable computer.

[0133] Item 27. The computer program according to Item 26, wherein the machine learning algorithm comprises a deep learning algorithm, for example a deep neural networks algorithm.

[0134] Item 28. A drug or combination of drugs for use in the prevention or treatment of a proliferative disease, wherein said treatment comprises performing the method according to any one of Items 1 to 23, preferably further comprising the step of providing a subject-specific and / or personalized prevention or treatment.

[0135] Item 29. The drug or combination of drugs for use according to Item 28, wherein the neoplastic disease or tumor is a drug-resistant neoplastic disease or tumor.

[0136] Item 30. A testing system comprising means for performing the method according to any one of Items 1 to 20, comprising a) a unit for culturing an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample derived from the subject as provided, b) a drug testing unit for contacting said array of said 3D microtissues with at least one drugs or combination of drugs to be tested, c) a unit for determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, comprising i) a first analysis unit for a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, ii) a second analysis unit for determining a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined.

[0137] Item 32. The testing system according to Item 31, further comprising at least one database for collecting and / or storing data regarding the responsome profile as identified, and optionally additional data with respect to the pre-selection and / or grouping of the combination of drugs, physiological parameters, data with respect to the effect(s) of said drugs or combination of drugs thereof, data with respect to the cellular and / or tissue pathway-specific responsome analysis, data with respect to adverse effects, data with respect to additional clinical parameters, such as patient specific data and / or treatment history, data with respect to the stratification and / or monitoring, and data for automatization of the system, for example for controlling at least one robot.

[0138] Item 33. Use of the testing system according to Item 31 or 32 or the computer program according to Item 26 or 27 for identifying at least one subject-specific drug or combination of drugs and / or for at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugsaccording to the method according to any one of Items 1 to 20.

[0139] The invention will now be described further in the following examples with reference to the accompanying figures, nevertheless, without being limited thereto. For the purposes of the present invention, all references as cited are incorporated by reference in their entireties. Figure 1 shows the schematic outline of a preferred embodiment of the method according to the present invention. Here, a lung cancer tissue resection was obtained, dissociated to generate a single cell solution and distributed with a defined cell number in a non-adhesive, u-bottom multi-well plate to allow for gravity-enforced tumor tissue formation. After microtumor maturation of 5 days, microtumors were treated with either immune-checkpoint inhibitor monotherapies or in combination with chemotherapies and the response monitored over 14 days. Drug response was analyzed via image-based (non-disruptive) measuring of growth kinetics with subsequent RNA isolation of the very same specimen to analyze transcriptomic changes. RNA was isolated the RNAeasy isolation and QC controlled.

[0140] Figure 2 shows an overview over the method for producing microtumors from fresh tumor tissue to maintain the original cell composition. A tissue sample from a non-small cell lung cancer was enzymatically digested. The resulting cell suspension was distributed in non- adhesive, round bottom multi-well plates to allow for gravity-enforced cell assembly. Over 6 days the cells re-formed tumor microtissues of 250 - 300 pm in a consistent size. Prior dosing some microtumors were again dissociated and used for FACS analysis to identify whether the different cell types from the native tissue are still present. Facs analysis demonstrates that next to the epithelial cancer cells the stromal connective tissue cells as well as the immune system were maintained.

[0141] Figure 3 shows an example of a pathway-specific responsome analysis of NSCLC cancer pathway. Evaluation of relative differential expression levels (log(treatment) / log(control)) of a subset of a non-small-cell lung cancer (NSCLC) pathway genes in relation to functional response analysis determined by effect of different drug / drug combination treatments of immuno-oncological drugs (immuno), chemotherapy combinations (chemo combo) or combinations of immuno-oncology drugs and chemotherapies (chemo / immuno) on the growth of 3D microtumors.

[0142] Figure 4 shows an example of pathway- specific responsome analysis. Ras-associated protein-1 (Rapl), a small GTPase in the Ras-related protein family, is an important regulator of basic cellular functions and plays many roles during cell invasion and metastasis in different cancers. Differential expression analysis of control samples vs. different cancer therapies (2 immuno- oncology drugs, 2 combinations of 2 chemotherapeutics (1.1 / 1.2 with different drug concentration ratios) and 2 combinations of one chemotherapy and one immune-oncology drug)

[0143] RECTIFIED SHEET (RULE 91) ISA / EP show most prominent changes (A), genes marked in grey boxes) in parts of the Rapl pathway regulating cell adhesion, migration and polarity in samples treated with chemotherapies but not in samples treated with Immuno-oncology drugs. B) and C) show the data as obtained.

[0144] Figure 5 shows the responsome principle of the method according to the present invention versus a common analysis.

[0145] Figure 6 shows an example for an analysis of a drug response vs. drug induced expression.

[0146] Figure 7 shows an example for an analysis of DNA vs. drug induced expression.

[0147] Examples

[0148] Identification of elimination half-life (tA) for drugs to be tested

[0149] The elimination half-life or drug half-life ti / 2 generally refers to the time required for half the dose of drug administered to be removed from the body. For those drugs to be tested, where a suitable ti / 2 can not be taken from the literature, and / or for the purpose of grouping or forming panels of drugs, ti / 2 values may be determined as follows.

[0150] In accordance with the existing guidelines, the rate (Cmax, tmax) and extent (AUC) of absorption of drug from a test formulation (vs. reference formulation) is evaluated using a single dose study in healthy volunteer subjects, followed by measuring the blood / plasma concentration of the parent drug and any major active metabolites (if present) for a time period of > 3 t ’A. tmax is usually related to the time period / length of t A, as drugs with short half-lives tend to peak and are eliminated quickly, often requiring more frequent dosing or redosing to maintain a drug within its clinically effective therapeutic range.

[0151] For a determination of the parameters, blood samples are taken at 0, 1, 2, 3, 4, 5, 6, 7, and 8 hours, optionally followed by 10 and 12, 18, 24, 30, 36, and 42 hours after administration. As a general rule, the more frequently samples around the expected time of the maximum concentration are taken, the more accurate the value of 1 1 / 2 (and tmax and Cmax) are. The above determinations can be made in several individuals, and median values can be determined as parameter e.g. for grouping, and testing. Producing microtumors from fresh tumor tissue to maintain the original cell composition

[0152] A tissue sample from a non-small cell lung cancer was enzymatically digested. The resulting cell suspension was distributed in non-adhesive, round bottom multi-well plates to allow for gravity-enforced cell assembly. Over 6 days the cells re-formed tumor microtissues of 250 - 300um in size. Prior dosing some microtumors were again dissociated and used for FACS analysis to identify whether the different cell types from the native tissue are still present. Facs analysis demonstrates that next to the epithelial cancer cells the stromal connective tissue cells as well as the immune system were maintained.

[0153] Example of colon cancer responsome analysis

[0154] A colon primary tumor tissue sample was obtained, dissociated to generate a single cell solution and distributed with a defined cell number in a non-adhesive, u-bottom multi-well 32 well plate to allow for gravity-enforced tumor tissue formation.

[0155] After microtumor maturation of 5 days, microtumors have a consistent size of approximately 250 pm and were treated with either immune-monotherapies or in combination with chemotherapies at the respective Cmax concentrations, and the response was monitored over 14 days. Controls were left untreated.

[0156] For determining the responsome profile for the 3D microtumors of the array and the drugs and / or combinations of drugs the non-disruptive kinetic-based functional analysis consisted of the absolute number of cells, the proliferation rate, and growth kinetics, as well as the size of cells, and the compactness.

[0157] Afterwards, molecular level parameter-based analysis of the 3D microtumors was performed on the array, and the analysis comprised transcriptome analysis, in particular of genes known to be involved in resistance and a pathway-specific responsome analysis with respect to Ras- associated protein- 1 (Rapl) (see also Figure 4, and Looi CK, et al. The Role of Ras- Associated Protein 1 (Rapl) in Cancer: Bad Actor or Good Player? Biomedicines. 2020 Sep 7;8(9):334. doi: 10.3390 / biomedicines8090334. PMID: 32906721; PMCID: PMC7555474).

[0158] Then, the responsome profile was determined. While the immune-monotherapies or the combination chemotherapies at the respective Cmax concentrations showed more or less equal effects on the absolute number of cells (reduction), the proliferation rate (reduction), and growth kinetics (slower), as well as the size of cells (smaller and mis-shaped), and the compactness (looser), the resistance genes were also equally distributed, only the inclusion of the pathwayspecific responsome analysis with respect to Ras-associated protein- 1 (Rapl) showed a clear preference for chemotherapy but not for immuno-oncology treatment. This “bias” for the treatment would not have been identified with a regular analysis.

[0159] Thus, identified was a specific and effective chemotherapy combination of drugs in the context of a potentially metastatic colon cancer case.

[0160] Similar experiments with the substantially identical results were performed with samples from sarcoma, liver cancer, and cervical cancer.

[0161] Example of lung cancer responsome analysis

[0162] A lung cancer tissue resection was obtained, dissociated to generate a single cell solution and distributed with a defined cell number in a non-adhesive, u-bottom multi-well plate to allow for gravity-enforced tumor tissue formation. After microtumor maturation of 5 days, microtumors were treated with either immune-checkpoint inhibitor monotherapies or in combination with chemotherapies and the response monitored over 14 days. Drug response was analyzed via image-based (non-disruptive) measuring of growth kinetics with subsequent RNA isolation of the very same specimen to analyze transcriptomic changes. RNA was isolated the RNAeasy isolation and QC controlled.

[0163] Pathway-specific responsome analysis of NSCLC cancer pathway

[0164] EGFR to RAS and ERK signaling pathway

[0165] Evaluation of relative differential expression levels (log(treatment) / log(control)) of a subset of a non-small-cell lung cancer (NSCLC) pathway genes in relation to functional response analysis determined by effect of different drug / drug combination treatments of immuno -oncological drugs (immuno), chemotherapy combinations (chemo-combo) or combinations of immuno- oncology drugs and chemotherapies (chemo / immuno) on the growth of 3D microtumors.

[0166] Example of pathway-specific responsome analysis

[0167] Rapl signaling pathway

[0168] Ras-associated protein-1 (Rapl), a small GTPase in the Ras-related protein family, is an important regulator of basic cellular functions and plays many roles during cell invasion and metastasis in different cancers (Figure 4 A). Differential expression analysis of control samples vs. different cancer therapies (2 immuno-oncology drugs, 2 combinations of 2 chemotherapeutics (1.1 / 1.2 with different drug concentration ratios) and 2 combinations of one chemotherapy and one immune-oncology drug) show most prominent changes (genes marked in grey boxes) in parts of the Rapl pathway regulating cell adhesion, migration and polarity in samples treated with chemotherapies but not in samples treated with Immuno-oncology drugs (Figure 4 B).

Claims

Claims1. A method for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject, comprising the steps of a) Providing an array of i) primary proliferative disease tissue samples and / or ii) proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, from a subject having or being diagnosed for a proliferative disease in a multi-well format in a suitable culture medium, wherein the multi-well format comprises between 6 wells and a number of wells corresponding to the drugs and / or combinations of drugs and doses thereof to be tested plus controls, b) Contacting the 3D microtissues, in particular 3D microtumors or organoids of the array with a predefined number of drugs and / or combinations of drugs and predefined doses thereof for a predefined amount of time or predefined different amounts of time, c) Determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, based on i) a first step of a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of absolute number of cells, proliferation rate, growth kinetics, density or compactness of cells, degree of dissolution of cells, morphological changes of the cells, size of cells, cellular composition of the cells, tissues, tumors or organoids, and metabolic rate, and ii) a second step of a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, wherein said analysis comprises at least one analysis selected from the group consisting of transcriptome, proteome, genome, metabolome, genetic mutation, and selected biomarker level analysis, wherein i) and ii) are performed on the same 3D microtissue, in particular the 3D microtumor or organoid of the array, and d) Identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined in step c).

2. The method according to claim 1, wherein determining the responsome profile further comprises at least one analysis selected from the group consisting of i) for non-disruptive kinetic-based functional analysis, cell count, size determination of said 3D microtumor or organoid, determination of the LDH release in said 3D microtumor or organoid, and determination of pre-selected biomarkers in said 3D microtumor or organoid, intracellular calcium level, physiological parameters that directly indicate the absolute number of cells, the proliferation rate, and the metabolic rate, wherein preferably said size determination of said 3D microtumor or organoid comprises at least one parameter selected from diameter, perimeter, volume, and area of optical cross section, and wherein preferably said size determination of said 3D microtumor or organoid comprises the use of an imaging device, and optionally further comprising the analysis of growth-kinetics, and ii) for the molecular level parameter analysis, gene expression level, protein level, metabolite level / amount, histochemistry, preselected biomarker levels, such as, for example, PD-L1 expression level, tumor mutational burden (TMB), microsatellite instability (MSI), and mismatch-repair deficiency (dMMR).

3. The method according to claim 1 or 2, wherein the method further comprises at least one spatial biology assay in order to evaluate the spatial context of cells within the tissue(s), and wherein said identifying is furthermore based to at least in part on the on the data as generated in said at least one spatial biology assay.

4. The method according to any one of claims 1 to 3, further comprising the step of e) selecting a drug or combination of drugs as specific and effective, and in particular personalized, against a proliferative disease in a subject based on the identifying in step d).

5. The method according to any one of claims 1 to 4, wherein steps a) to d) are repeated for at least once comprising combining the drugs as identified in the first round of the method.

6. The method according to any one of claims 1 to 5, wherein the combination of drugs comprises at least two drugs that are applied jointly or sequentially.

7. The method according to any one of claims 1 to 6, wherein the 3D microtissues, in particular 3D microtumors or organoids of the array are contacted with the drug or combination of drugs at the Cmax concentration(s), and / or wherein the drugs or combination of drugs is / are contacted with the 3D microtissues, in particular 3D microtumors or organoids of the array for a time span depending at least in part on their t / 2 value(s).

8. The method according to any one of claims 1 to 7, wherein before the contacting in step b) the primary proliferative disease tissue samples or the proliferative disease-derived 3D microtissues, in particular 3D microtumors or organoids, comprise cells reflecting the composition of the major cell types of the native tumor tissue, such as, for example, connective tissue cells, CD4+ T lymphocytes, epithelial cancer cells, macrophages, natural killer cells, B cells, and CD8+ T lymphocytes.

9. The method according to any one of claims 1 to 8, wherein the proliferative disease is selected from neoplastic diseases, benign proliferative diseases, malign proliferative diseases, psoriasis, cancer, such as, for example, lung cancer, cervical cancer, brain cancer, gastric cancer, liver cancer, bone cancer, head and neck cancer, benign tumours, adenomas, endometriosis, hyperplasia (e.g. prostate), thyroid nodules, haemangioma, lymphangioma, keloids, warts, and colon cancer.

10. The method according to any one of claims 1 to 9, wherein the providing of said 3D microtissues, in particular 3D microtumors or organoids comprises a maturation time of about 6 hours to 7 days, preferably about 1 to 6 days, more preferably about 2 to 5 days, and / or wherein said 3D microtumors as generated have a size of 100-450 pm, preferably 350 pm + / - 100 pm.

11. The method according to any one of claims 1 to 10, further comprising at least one cellular and / or tissue pathway-specific responsome analysis based on the molecular level parameter-based analysis in step c), and identifying at least one functional relationship between genes and / or proteins of a pathway and the neoplastic disease or tumor and / or the drug or combination of drugs, optionally further comprising the step of identifying genes and / or proteins of the pathway as drug targets.

12. The method according to any one of claims 1 to 11, wherein the responsome profile as identified is stored on a centralized server, and / or the identifying comprises at least one of i) comparing the responsome profile with a responsome profile generated from tissue samples of a healthy subject, ii) comparing the responsome profile with a responsome profile generated from tissue samples of other subjects diagnosed with a proliferative disease, treated or untreated, wherein preferably said identifying comprises a method using a deep learning algorithm, wherein preferably the algorithm is trained using the stored responsome profiles.

13. A method for identifying adverse effects associated with a treatment with a patientspecific drug or combination of drugs in a subject, comprising performing the method according to any one of claims 1 to 12, further comprising the step of testing and analyzing said subject-specific drug or combination of drugs for adverse effects in said patient, or a method for stratifying a patient with respect to a treatment with a patientspecific drug or combination of drugs, comprising performing the method according to any one of claims 1 to 12, comprising identifying at least one subject-specific drug or combination of drugs based on the responsome profile, and optionally further based on the molecular pathway, as determined, and stratifying of said subject based on said subject-specific drug or combination of drugs as identified, or a method for monitoring the treatment with a subject-specific drug or combination of drugs, comprising performing the method according to any one of claims 1 to 12, treating said patient with said at least one patient-specific drug or combination of drugs as identified, and repeating the method according to any one of claims 1 to 12 on a sample of said subject following the treatment, and optionally adjusting the treatment based on the result(s) of said repeating.

14. The method according to any one of claims 1 to 12, wherein said step of identifying and / or selecting includes the use of additional clinical parameters, such as patient specific data and / or treatment history.

15. A computer program for performing the method according to claim 12 on a suitable computer, wherein preferably the machine learning algorithm comprises a deep learning algorithm, for example a deep neural networks algorithm.

6. A testing system comprising means for performing the method according to any one of claims 1 to 12, comprising a) a unit for culturing an array of 3D microtissues, such as microtumors or organoids, based on dissociated cells of a tissue sample derived from the subject as provided, b) a drug testing unit for contacting said array of said 3D microtissues with at least one drugs or combination of drugs to be tested, c) a unit for determining a responsome profile for the 3D microtissues, in particular for the 3D microtumors or organoids of the array and the drugs and / or combinations of drugs and predefined doses and times thereof as tested, comprising i) a first analysis unit for a non-disruptive kinetic-based functional analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, ii) a second analysis unit for determining a molecular level parameter-based analysis of the 3D microtissues, in particular of the 3D microtumors or organoids of the array, and d) a unit for identifying a specific and effective, in particular personalized, drug or combination of drugs against a proliferative disease in a subject based on the responsome profile as determined, optionally further comprising at least one database for collecting and / or storing data regarding the responsome profile as identified, and optionally additional data with respect to the pre-selection and / or grouping of the combination of drugs, physiological parameters, data with respect to the effect(s) of said drugs or combination of drugs thereof, data with respect to the cellular and / or tissue pathway-specific responsome analysis, data with respect to adverse effects, data with respect to additional clinical parameters, such as patient specific data and / or treatment history, data with respect to the stratification and / or monitoring, and data for automatization of the system, for example for controlling at least one robot.

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